Curriculum learning Recently there has been a lot of work on using automated curricula to speed up exploration in single-agent tasks [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. Environments or goals may be generated as in this work using RL [2]}, [7]}, [8]}, [9]}, or using GANs [1]}, evolutionary algorithms [4]}, [5]} or Gaussian mixture models [3]}. Selection criteria for environments include intermediate student performance [1]}, [4]}, [7]}, learning progress [2]}, [3]} and regret [8]}, [9]}, but neither of these selection criteria has been generalized to or tested in multi-agent environments. Initial-state value disagreement [6]} and value prediction error [10]} has been previously used to rerank randomly sampled goals or environments.
